instruction: Instruction

In rlme: Rank-Based Estimation and Prediction in Random Effects Nested Models

Description

A data frame on school instruction results.

Format

A data frame with 1190 observations on the following 13 variables.

X

a numeric vector

girl

a numeric vector

minority

a numeric vector

mathkind

a numeric vector

mathgain

a numeric vector

ses

a numeric vector

yearstea

a numeric vector

mathknow

a numeric vector

housepov

a numeric vector

mathprep

a numeric vector

classid

a numeric vector identifying the class within school

schoolid

a numeric vector identifying the school

childid

a numeric vector

Source

West, B., Welch, K. B., & Galecki, A. T. (2006). Linear mixed models: a practical guide using statistical software. Chapman & Hall/CRC.

Examples

# The following code takes a few minutes to run.
# In the interest of saving CRAN's example testing time,
# it has been commented out. If you want to use it,
# just uncomment and run.

# data(instruction)
# attach(instruction)

# data = data.frame(
#   y = mathgain,
#   mathkind = mathkind, 
#   girl = girl,
#   minority = minority,
#   ses = ses, 
#   school = factor(schoolid), 
#   section = factor(classid))


# fit.rlme = rlme(y ~ 1 + mathkind + girl + minority + ses + (1 | school) + (1 | school:section),
#  data = data,
#  method = "gr")
  
# summary(fit.rlme)

rlme documentation built on May 2, 2019, 3:47 p.m.